Educational Data Mining is a leading international forum for high-quality research that mines datasets to answer educational research questions, including exploring how people learn and how they teach. These data may originate from a variety of learning contexts, including learning and information management systems, interactive learning environments, intelligent tutoring systems, educational games, and data-rich learning activities. Educational data mining considers a wide variety of types of data, including but not limited to log files, student-produced artifacts, discourse, learning content and context, sensor data, and multi-resource and multimodal streams. The overarching goal of the Educational Data Mining research community is to support learners and teachers more effectively, by developing data-driven understandings of the learning and teaching processes in a wide variety of contexts and for diverse learners.

The 17th iteration of the conference, EDM 2024, will take place in a hybrid format, both online and in-person, to facilitate participation and networking for all.

The theme of this year’s conference is “New tools, new prospects, new risks – educational data mining in the age of generative AI”. This year’s theme focuses on the movement from descriptive and predictive models to generative artificial intelligence (AI) and what that means for learning environments and processes. While the new methods unlock exciting new potentials for educational data mining, they also foreground many ethical considerations and risks that are associated with all types of machine learning and artificial intelligence. In addition to the general topics listed below, we welcome research in the following areas: mitigating biases and harms that may result from model use, accounting for the stereotypes that are inherent to the large models that drive generative AI, separating the hype surrounding these new technologies from their potential in educational settings, and finding ways to use these models to better understand learning processes and support learning.

Topics of Interest
Topics of interest to the conference include but are not limited to:

Developing new techniques for mining educational data.
Closing the loop between EDM research and learning sciences
Informing data mining research with educational and/or motivational theories
Actionable advice rooted in educational data mining research, experiments, and outcomes
Evaluating the efficacy of curriculum and interventions
Domain Knowledge Modeling
Deriving representations of domain knowledge from data
Algorithms for discovering relationships, associations, and prerequisite structures between learning resources with different formats, including programming practices, essays, and videos
Algorithms to improve existing domain models
Novel methods to collect domain knowledge models, including crowd-sourcing and expert tagging
Educational Recommenders, Instructional Sequencing, and Personalized Learning
Learning resource recommendation algorithms, remedial recommendations, and learner choice in selecting the next activity
Goal-oriented instructional sequencing
Personalized course recommendations
Peer recommendation for collaborative learning
Offline and online evaluation methods for educational recommender systems and sequencing algorithms
Equity, Privacy, Transparency, and Fairness
Ethical considerations in EDM
Legal and social policies to govern EDM
Developing privacy-protecting EDM algorithms and detecting learner privacy violations in existing methods
Developing and applying fairer learning algorithms, and detecting and correcting instances of algorithmic unfairness in existing methods
Developing, improving, and evaluating explainable EDM algorithms
Learner Cognitive and Behavior Modeling and its association with performance
Modeling and detecting students’ affective and cognitive states (e.g., engagement, confusion) with multimodal data
Temporal patterns in student behavior including gaming the system, procrastination, and sequence modeling
Data mining to understand how learners interact with various pedagogical environments such as educational games and exploratory learning environments
Learner Knowledge and Performance Modeling
Automatically assessing student knowledge
Learner knowledge gain and forgetting models in domains with complex concept structures
Modeling real-world problem-solving in open-ended domains
Causal inference of students’ learning
Predicting students’ future performance
Learning analytics
Institutional analytics
Learner profiling
Multimodal analytics
Social and Collaborative Learning
Modeling student and group verbal and non-verbal interactions for collaborative and/or competitive problem-solving
Social network analysis of student and teacher interactions
Data mining to understand how learners interact in formal and informal educational contexts
Peer-assessment modeling
Social learner modeling
Reproducibility
Replicating previous studies with larger sample sizes, in different domains, and/or in more diverse contexts
Facilitating accessible benchmarking systems and publishing educational datasets that are useful for the community